Robust detection of copy-move forgery using texture features
Why this work is in the frame
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Bibliographic record
Abstract
Summary from only given. Cloning or copy-move is a special type of forgery that tries to hide the regions of the image by a block that is copied from the same image. In this paper we introduce a new texturebased method to detect such forgeries in digital images. In the proposed approach, at first the image is divided into some overlapping blocks and then we utilize modified Gabor filter to extract the feature vector of each block. In the next step, to reduce the dimension of the extracted feature vectors, PCA algorithm is applied on them. Finally, in the matching step, we use counting bloom filter to determine similar or duplicated blocks. The experimental results show that the proposed features are very effective in accurate detection of copied regions, even when these regions have undergone lossy compression. The accuracy and performance of our method in comparison to similar works, proves efficiency of the proposed theory and shows noticeable increase in detection rate.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it